datasets import load_iris from sklearn.model_selection import train_test_split iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 创建LGBMClassifier模型使用LightGBM库中的LGBMClassifier类创建模型。
3. 示例代码 下面的示例代码展示了如何使用LGBMClassifier进行分类任务,并设置上述参数: AI检测代码解析 importnumpyasnpimportpandasaspdfromlightgbmimportLGBMClassifierfromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score# 加载数据集data=load_iris...
# 设置模型参数model=LGBMClassifier(n_estimators=100,learning_rate=0.1,max_depth=-1,# 默认无限深num_leaves=31,objective='multiclass',class_weight='balanced') 1. 2. 3. 4. 5. 6. 7. 5. 训练模型 使用训练数据集来训练模型。 AI检测代码解析 # 拆分数据集为训练集和测试集fromsklearn.model_sel...
▪ XGBoost，显示功能重要性并解释XGBClassifier，XGBRegressor和xgboost.Booster的预测。▪ LightGBM，显示功能重要性并解释LGBMClassifier和LGBMRegressor的预测。▪ lightning，解释闪电分类器和回归量的权重和预测。▪ sklearn-crfsuite。ELI5允许检查sklearn_crfsuite.CRF模型的权重。ELI5还实现了几种检测黑盒模型的...
from lightgbm import LGBMClassifier from xgboost import XGBClassifier # 模型评估 from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import confusion_matrix, accuracy_score, classification_report from sklearn.metrics import roc_auc_score, roc_curve, scorer from sk...
# 导入模块importlightgbmaslgb# LGB算法fromsklearn.externalsimportjoblib# 模型训练model_lgb = lgb.LGBMClassifier(boosting_type='gbdt',# gbdt 梯度提升决策树metric ='auc', n_estimators =180, learning_rate =0.05, is_unbalance ='true', objective ='binary',# 任务分类、回归random_state =1, ...
要将LightGBM的lgb.LGBMClassifier模型权重转换为ONNX格式,你可以按照以下步骤进行操作: 1. 训练LightGBM的lgb.LGBMClassifier模型,并保存模型权重 首先,你需要训练一个LightGBM模型并保存其权重。这里是一个简单的例子: python import lightgbm as lgb from sklearn.datasets import load_iris from sklearn.model_selecti...
from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=123) from lightgbm import LGBMClassifier model=LGBMClassifier() model.fit(X_train,y_train) #模型评估及预测 ...
### 训练文本分类模型fromsklearn.model_selectionimporttrain_test_splitfromlightgbmimportLGBMClassifierfromsklearn.linear_modelimportLogisticRegressiontrain_x,test_x,train_y,test_y=train_test_split(sents_vec,spam_df.label,test_size=0.2,shuffle=True,random_state=42)result=[]clf=LGBMClassifier(class_wei...
KNeighborsClassifierfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.ensemble import RandomForestClassifierfrom lightgbm import LGBMClassifier# 预处理from sklearn.preprocessing import StandardScaler, MinMaxScaler# 模型评估from sklearn.model_selection import train_test_split, GridSearchCVfrom sklearn....